ArticleGenome biology2024
TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- ELF5-Mediated Enhancer-Promoter Interaction Regulates LALBA Expression in Ovine Mammary Gland.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.Genome biology · 2024Article
- Advancing Regulatory Genomics With Machine Learning.Bioinformatics and biology insights · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.